Cameras are commonly used to capture an image of a scene that includes one or more objects. Unfortunately, some of the images are blurred. For example, movement of the camera and/or movement of the objects in the scene during the exposure time of the camera can cause the image to be blurred. Further, if the camera is not properly focused when the image is captured, that image will be blurred.
Currently, there are certain methods that are used to determine whether the image is blurred. Unfortunately, many of these methods associate blur degree with edge spreading in the image. As a result thereof, their performance is rather sensitive to the accuracy of edge detection techniques. For a sharp and low noise image, the current edge detection techniques can achieve relatively good results. However, this performance degrades significantly for other types of images. For example, it has been complicated to attain robust performance for specific image types, such as macros, close-up portraits and night scenes. Additionally, it has also been difficult to achieve robust performance for large scale testing sets that cover multiple image types and various camera settings.